In our study, the discovery methods that identify the subgroups (e.g. anomalous subsets) are inherently
model-agnostic search algorithms. We work with a similar research question: What features describing the individual would need to change to achieve the desired output?  [1]} However, we aim to identify the exact changes that can be made to the characteristics of a subgroup of individual to achieve a desired output (in our case, a significant change in the anomalous score). Our objective is therefore slightly different from traditional counterfactual explanation, in two ways: i) Given a subgroup that has been identified as anomalous (e.g, having a significantly higher rate of an outcome than a global average), we seek to find the least number of perturbations to the subgroup's features and feature values of that would lead to loss of anomalousness; and ii) As opposed to finding the counterfactual example, we find the feature values from the complement subset.
